
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import os
import glob
%matplotlib inline


#load the data 
df = pd.read_csv('prosperLoanData.csv')


#explore the data
pd.set_option('display.max_columns', None)
df.head(10)


#get more info. about the data 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113937 non-null  object 
 1   ListingNumber                        113937 non-null  int64  
 2   ListingCreationDate                  113937 non-null  object 
 3   CreditGrade                          28953 non-null   object 
 4   Term                                 113937 non-null  int64  
 5   LoanStatus                           113937 non-null  object 
 6   ClosedDate                           55089 non-null   object 
 7   BorrowerAPR                          113912 non-null  float64
 8   BorrowerRate                         113937 non-null  float64
 9   LenderYield                          113937 non-null  float64
 10  EstimatedEffectiveYield              84853 non-null   float64
 11  EstimatedLoss                        84853 non-null   float64
 12  EstimatedReturn                      84853 non-null   float64
 13  ProsperRating (numeric)              84853 non-null   float64
 14  ProsperRating (Alpha)                84853 non-null   object 
 15  ProsperScore                         84853 non-null   float64
 16  ListingCategory (numeric)            113937 non-null  int64  
 17  BorrowerState                        108422 non-null  object 
 18  Occupation                           110349 non-null  object 
 19  EmploymentStatus                     111682 non-null  object 
 20  EmploymentStatusDuration             106312 non-null  float64
 21  IsBorrowerHomeowner                  113937 non-null  bool   
 22  CurrentlyInGroup                     113937 non-null  bool   
 23  GroupKey                             13341 non-null   object 
 24  DateCreditPulled                     113937 non-null  object 
 25  CreditScoreRangeLower                113346 non-null  float64
 26  CreditScoreRangeUpper                113346 non-null  float64
 27  FirstRecordedCreditLine              113240 non-null  object 
 28  CurrentCreditLines                   106333 non-null  float64
 29  OpenCreditLines                      106333 non-null  float64
 30  TotalCreditLinespast7years           113240 non-null  float64
 31  OpenRevolvingAccounts                113937 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113937 non-null  float64
 33  InquiriesLast6Months                 113240 non-null  float64
 34  TotalInquiries                       112778 non-null  float64
 35  CurrentDelinquencies                 113240 non-null  float64
 36  AmountDelinquent                     106315 non-null  float64
 37  DelinquenciesLast7Years              112947 non-null  float64
 38  PublicRecordsLast10Years             113240 non-null  float64
 39  PublicRecordsLast12Months            106333 non-null  float64
 40  RevolvingCreditBalance               106333 non-null  float64
 41  BankcardUtilization                  106333 non-null  float64
 42  AvailableBankcardCredit              106393 non-null  float64
 43  TotalTrades                          106393 non-null  float64
 44  TradesNeverDelinquent (percentage)   106393 non-null  float64
 45  TradesOpenedLast6Months              106393 non-null  float64
 46  DebtToIncomeRatio                    105383 non-null  float64
 47  IncomeRange                          113937 non-null  object 
 48  IncomeVerifiable                     113937 non-null  bool   
 49  StatedMonthlyIncome                  113937 non-null  float64
 50  LoanKey                              113937 non-null  object 
 51  TotalProsperLoans                    22085 non-null   float64
 52  TotalProsperPaymentsBilled           22085 non-null   float64
 53  OnTimeProsperPayments                22085 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64
 56  ProsperPrincipalBorrowed             22085 non-null   float64
 57  ProsperPrincipalOutstanding          22085 non-null   float64
 58  ScorexChangeAtTimeOfListing          18928 non-null   float64
 59  LoanCurrentDaysDelinquent            113937 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113937 non-null  int64  
 62  LoanNumber                           113937 non-null  int64  
 63  LoanOriginalAmount                   113937 non-null  int64  
 64  LoanOriginationDate                  113937 non-null  object 
 65  LoanOriginationQuarter               113937 non-null  object 
 66  MemberKey                            113937 non-null  object 
 67  MonthlyLoanPayment                   113937 non-null  float64
 68  LP_CustomerPayments                  113937 non-null  float64
 69  LP_CustomerPrincipalPayments         113937 non-null  float64
 70  LP_InterestandFees                   113937 non-null  float64
 71  LP_ServiceFees                       113937 non-null  float64
 72  LP_CollectionFees                    113937 non-null  float64
 73  LP_GrossPrincipalLoss                113937 non-null  float64
 74  LP_NetPrincipalLoss                  113937 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64
 76  PercentFunded                        113937 non-null  float64
 77  Recommendations                      113937 non-null  int64  
 78  InvestmentFromFriendsCount           113937 non-null  int64  
 79  InvestmentFromFriendsAmount          113937 non-null  float64
 80  Investors                            113937 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 68.1+ MB


#get familiar with data
df.describe()


# check for null values
df.isnull().sum()

ListingKey                         0
ListingNumber                      0
ListingCreationDate                0
CreditGrade                    84984
Term                               0
                               ...  
PercentFunded                      0
Recommendations                    0
InvestmentFromFriendsCount         0
InvestmentFromFriendsAmount        0
Investors                          0
Length: 81, dtype: int64


# check for duplicates
df.duplicated().sum()

0


# make a copy from the date to clean and work with it
loan = df.copy()


# remove the null values raws in APR
loan = loan[loan['BorrowerAPR'].isnull() == False]
loan.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 113912 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113912 non-null  object 
 1   ListingNumber                        113912 non-null  int64  
 2   ListingCreationDate                  113912 non-null  object 
 3   CreditGrade                          28928 non-null   object 
 4   Term                                 113912 non-null  int64  
 5   LoanStatus                           113912 non-null  object 
 6   ClosedDate                           55064 non-null   object 
 7   BorrowerAPR                          113912 non-null  float64
 8   BorrowerRate                         113912 non-null  float64
 9   LenderYield                          113912 non-null  float64
 10  EstimatedEffectiveYield              84853 non-null   float64
 11  EstimatedLoss                        84853 non-null   float64
 12  EstimatedReturn                      84853 non-null   float64
 13  ProsperRating (numeric)              84853 non-null   float64
 14  ProsperRating (Alpha)                84853 non-null   object 
 15  ProsperScore                         84853 non-null   float64
 16  ListingCategory (numeric)            113912 non-null  int64  
 17  BorrowerState                        108422 non-null  object 
 18  Occupation                           110349 non-null  object 
 19  EmploymentStatus                     111682 non-null  object 
 20  EmploymentStatusDuration             106312 non-null  float64
 21  IsBorrowerHomeowner                  113912 non-null  bool   
 22  CurrentlyInGroup                     113912 non-null  bool   
 23  GroupKey                             13323 non-null   object 
 24  DateCreditPulled                     113912 non-null  object 
 25  CreditScoreRangeLower                113346 non-null  float64
 26  CreditScoreRangeUpper                113346 non-null  float64
 27  FirstRecordedCreditLine              113240 non-null  object 
 28  CurrentCreditLines                   106333 non-null  float64
 29  OpenCreditLines                      106333 non-null  float64
 30  TotalCreditLinespast7years           113240 non-null  float64
 31  OpenRevolvingAccounts                113912 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113912 non-null  float64
 33  InquiriesLast6Months                 113240 non-null  float64
 34  TotalInquiries                       112778 non-null  float64
 35  CurrentDelinquencies                 113240 non-null  float64
 36  AmountDelinquent                     106315 non-null  float64
 37  DelinquenciesLast7Years              112947 non-null  float64
 38  PublicRecordsLast10Years             113240 non-null  float64
 39  PublicRecordsLast12Months            106333 non-null  float64
 40  RevolvingCreditBalance               106333 non-null  float64
 41  BankcardUtilization                  106333 non-null  float64
 42  AvailableBankcardCredit              106393 non-null  float64
 43  TotalTrades                          106393 non-null  float64
 44  TradesNeverDelinquent (percentage)   106393 non-null  float64
 45  TradesOpenedLast6Months              106393 non-null  float64
 46  DebtToIncomeRatio                    105358 non-null  float64
 47  IncomeRange                          113912 non-null  object 
 48  IncomeVerifiable                     113912 non-null  bool   
 49  StatedMonthlyIncome                  113912 non-null  float64
 50  LoanKey                              113912 non-null  object 
 51  TotalProsperLoans                    22085 non-null   float64
 52  TotalProsperPaymentsBilled           22085 non-null   float64
 53  OnTimeProsperPayments                22085 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64
 56  ProsperPrincipalBorrowed             22085 non-null   float64
 57  ProsperPrincipalOutstanding          22085 non-null   float64
 58  ScorexChangeAtTimeOfListing          18928 non-null   float64
 59  LoanCurrentDaysDelinquent            113912 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113912 non-null  int64  
 62  LoanNumber                           113912 non-null  int64  
 63  LoanOriginalAmount                   113912 non-null  int64  
 64  LoanOriginationDate                  113912 non-null  object 
 65  LoanOriginationQuarter               113912 non-null  object 
 66  MemberKey                            113912 non-null  object 
 67  MonthlyLoanPayment                   113912 non-null  float64
 68  LP_CustomerPayments                  113912 non-null  float64
 69  LP_CustomerPrincipalPayments         113912 non-null  float64
 70  LP_InterestandFees                   113912 non-null  float64
 71  LP_ServiceFees                       113912 non-null  float64
 72  LP_CollectionFees                    113912 non-null  float64
 73  LP_GrossPrincipalLoss                113912 non-null  float64
 74  LP_NetPrincipalLoss                  113912 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113912 non-null  float64
 76  PercentFunded                        113912 non-null  float64
 77  Recommendations                      113912 non-null  int64  
 78  InvestmentFromFriendsCount           113912 non-null  int64  
 79  InvestmentFromFriendsAmount          113912 non-null  float64
 80  Investors                            113912 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 69.0+ MB


# correct the range of DebtToIncomeRatio
loan.DebtToIncomeRatio = loan.DebtToIncomeRatio.abs()
loan = loan.query('DebtToIncomeRatio <= 3')


# correct the ProsperScore 
loan = loan.query('ProsperScore <= 10')


# clean incorrect data in BankcardUtilization
loan = loan.query('BankcardUtilization <= 1')


# drop inefficient coulmns 
loan = loan.drop(['ListingKey','ListingNumber','ListingCreationDate','ClosedDate','GroupKey', 'CurrentlyInGroup', 'DateCreditPulled', 'CreditScoreRangeLower', 'CreditScoreRangeUpper', 'FirstRecordedCreditLine', 'CurrentCreditLines', 'OpenCreditLines','InquiriesLast6Months', 'PublicRecordsLast12Months', 'RevolvingCreditBalance', 'LoanKey', 'ProsperPrincipalBorrowed', 'ProsperPrincipalOutstanding', 'ScorexChangeAtTimeOfListing', 'LoanCurrentDaysDelinquent', 'LoanFirstDefaultedCycleNumber', 'LoanMonthsSinceOrigination', 'LoanNumber', 'LoanOriginationDate', 'MemberKey', 'LP_CollectionFees', 'LP_GrossPrincipalLoss', 'LP_NetPrincipalLoss', 'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount' ],axis=1)


loan.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 75446 entries, 1 to 113936
Data columns (total 51 columns):
 #   Column                               Non-Null Count  Dtype  
---  ------                               --------------  -----  
 0   CreditGrade                          0 non-null      object 
 1   Term                                 75446 non-null  int64  
 2   LoanStatus                           75446 non-null  object 
 3   BorrowerAPR                          75446 non-null  float64
 4   BorrowerRate                         75446 non-null  float64
 5   LenderYield                          75446 non-null  float64
 6   EstimatedEffectiveYield              75446 non-null  float64
 7   EstimatedLoss                        75446 non-null  float64
 8   EstimatedReturn                      75446 non-null  float64
 9   ProsperRating (numeric)              75446 non-null  float64
 10  ProsperRating (Alpha)                75446 non-null  object 
 11  ProsperScore                         75446 non-null  float64
 12  ListingCategory (numeric)            75446 non-null  int64  
 13  BorrowerState                        75446 non-null  object 
 14  Occupation                           74139 non-null  object 
 15  EmploymentStatus                     75446 non-null  object 
 16  EmploymentStatusDuration             75432 non-null  float64
 17  IsBorrowerHomeowner                  75446 non-null  bool   
 18  TotalCreditLinespast7years           75446 non-null  float64
 19  OpenRevolvingAccounts                75446 non-null  int64  
 20  OpenRevolvingMonthlyPayment          75446 non-null  float64
 21  TotalInquiries                       75446 non-null  float64
 22  CurrentDelinquencies                 75446 non-null  float64
 23  AmountDelinquent                     75446 non-null  float64
 24  DelinquenciesLast7Years              75446 non-null  float64
 25  PublicRecordsLast10Years             75446 non-null  float64
 26  BankcardUtilization                  75446 non-null  float64
 27  AvailableBankcardCredit              75446 non-null  float64
 28  TotalTrades                          75446 non-null  float64
 29  TradesNeverDelinquent (percentage)   75446 non-null  float64
 30  TradesOpenedLast6Months              75446 non-null  float64
 31  DebtToIncomeRatio                    75446 non-null  float64
 32  IncomeRange                          75446 non-null  object 
 33  IncomeVerifiable                     75446 non-null  bool   
 34  StatedMonthlyIncome                  75446 non-null  float64
 35  TotalProsperLoans                    16639 non-null  float64
 36  TotalProsperPaymentsBilled           16639 non-null  float64
 37  OnTimeProsperPayments                16639 non-null  float64
 38  ProsperPaymentsLessThanOneMonthLate  16639 non-null  float64
 39  ProsperPaymentsOneMonthPlusLate      16639 non-null  float64
 40  LoanOriginalAmount                   75446 non-null  int64  
 41  LoanOriginationQuarter               75446 non-null  object 
 42  MonthlyLoanPayment                   75446 non-null  float64
 43  LP_CustomerPayments                  75446 non-null  float64
 44  LP_CustomerPrincipalPayments         75446 non-null  float64
 45  LP_InterestandFees                   75446 non-null  float64
 46  LP_ServiceFees                       75446 non-null  float64
 47  LP_NonPrincipalRecoverypayments      75446 non-null  float64
 48  PercentFunded                        75446 non-null  float64
 49  Recommendations                      75446 non-null  int64  
 50  Investors                            75446 non-null  int64  
dtypes: bool(2), float64(35), int64(6), object(8)
memory usage: 28.9+ MB


loan.head()


# plotting the BorrowerAPR 
plt.figure(figsize=[8, 8])
plt.suptitle('The BorrowerAPR ditribution');
bins = np.arange(0, loan.BorrowerAPR.max()+0.02,0.02)
plt.hist(data=loan, x='BorrowerAPR', bins=bins);
plt.xlim(0,0.45);


# plotting the LoanOriginalAmount 
plt.figure(figsize=[8, 8])
plt.suptitle('The LoanOriginalAmount ditribution');
bins = np.arange(0, loan.LoanOriginalAmount.max()+1000,1000)
plt.hist(data=loan, x='LoanOriginalAmount', bins=bins);


# ploting OnTimeProsperPayments distribution
plt.figure(figsize=[8, 8])
plt.suptitle('The OnTimeProsperPayments ditribution');
bins = np.arange(0, loan.OnTimeProsperPayments.max()+5,5)
plt.hist(data=loan, x='OnTimeProsperPayments', bins=bins);
plt.xlim(0,150);


# ploting MonthlyLoanPayment distribution
plt.figure(figsize=[8, 8])
plt.suptitle('The MonthlyLoanPayment ditribution');
bins = np.arange(0, loan.MonthlyLoanPayment.max()+50,50)
plt.hist(data=loan, x='MonthlyLoanPayment', bins=bins);


# ploting DebtToIncomeRatio distribution
plt.figure(figsize=[8, 8])
plt.suptitle('The DebtToIncomeRatio ditribution');
bins = np.arange(0, loan.DebtToIncomeRatio.max()+0.2,0.2)
plt.hist(data=loan, x='DebtToIncomeRatio', bins=bins);


# ploting LoanOriginationQuarter distribution
plt.figure(figsize=[11, 8])
quarter = ['Q1 2007','Q2 2007','Q3 2007','Q4 2007','Q1 2008','Q2 2008','Q3 2008','Q4 2008','Q1 2009','Q2 2009','Q3 2009','Q4 2009','Q1 2010','Q2 2010','Q3 2010','Q4 2010','Q1 2011','Q2 2011','Q3 2011','Q4 2011','Q1 2012','Q2 2012','Q3 2012','Q4 2012','Q1 2013','Q2 2013','Q3 2013','Q4 2013','Q1 2014']
Qcat = pd.api.types.CategoricalDtype(ordered=True, categories=quarter)
df['LoanOriginationQuarter'] = df['LoanOriginationQuarter'].astype(Qcat)

default_color = sb.color_palette()[0]
sb.countplot(data = df, x = 'LoanOriginationQuarter', color = default_color)
plt.suptitle('The LoanOriginationQuarter ditribution');
plt.xticks(rotation=90);


# plotting the Term 
plt.figure(figsize=[8, 8])
default_color = sb.color_palette()[0]
sb.countplot(data = loan, x = 'Term', color = default_color)
plt.suptitle('The Term ditribution');


df.CreditGrade.value_counts()

C     5649
D     5153
B     4389
AA    3509
HR    3508
A     3315
E     3289
NC     141
Name: CreditGrade, dtype: int64


# plotting the CreditGrade 
plt.figure(figsize=[8, 8])
default_color = sb.color_palette()[0]
rquarter = ['AA','A','B','C','D','E','HR','NC']
bcat = pd.api.types.CategoricalDtype(ordered=True, categories=rquarter)
df['CreditGrade'] = df['CreditGrade'].astype(bcat)
sb.countplot(data = df, x = 'CreditGrade', color = default_color)
plt.suptitle('The CreditGrade ditribution');


# plotting the LoanStatus 
plt.figure(figsize=[8, 8])
default_color = sb.color_palette()[0]
sb.countplot(data = loan, x = 'LoanStatus', color = default_color)
plt.suptitle('The LoanStatus ditribution');
plt.xticks(rotation=90);


# plotting the ProsperScore 
plt.figure(figsize=[8, 8])
default_color = sb.color_palette()[0]
sb.countplot(data = loan, x = 'ProsperScore', color = default_color)
plt.suptitle('The ProsperScore ditribution');


# plotting the BorrowerState 
plt.figure(figsize=[11, 8])
default_color = sb.color_palette()[0]
sb.countplot(data = loan, x = 'BorrowerState', color = default_color)
plt.suptitle('The BorrowerState ditribution');
plt.xticks(rotation=90);


# plotting the IncomeVerifiable 
plt.figure(figsize=[8, 8])
default_color = sb.color_palette()[0]
sb.countplot(data = loan, x = 'IncomeVerifiable', color = default_color)
plt.suptitle('The IncomeVerifiable ditribution');


# plotting the IsBorrowerHomeowner 
plt.figure(figsize=[8, 8])
default_color = sb.color_palette()[0]
sb.countplot(data = loan, x = 'IsBorrowerHomeowner', color = default_color)
plt.suptitle('The IsBorrowerHomeowner ditribution');


# plotting the Occupation 
plt.figure(figsize=[15, 8])
default_color = sb.color_palette()[0]
freq = loan.Occupation.value_counts().index
sb.countplot(data = loan, x = 'Occupation', color = default_color, order=freq)
plt.suptitle('The Occupation ditribution');
plt.xticks(rotation=90);


# save the clean data to a .csv file

loan.to_csv('loan_clean.csv', index=False)


plt.figure(figsize=[11, 11]);
plt.suptitle('BorrowerARP with Borrower credit information');

# BorrowerAPR vs CreditGrade
plt.subplot(2, 2, 1);
base_color =  sb.color_palette()[0]
sb.boxplot(data=df, x='CreditGrade', y='BorrowerAPR', color=base_color);
plt.xlabel('CreditGrade');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs ProsperScore
plt.subplot(2, 2, 2);
sb.boxplot(data=loan, x='ProsperScore', y='BorrowerAPR', color=base_color);
plt.xlabel('ProsperScore');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs TotalCreditLinespast7years
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='TotalCreditLinespast7years', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('TotalCreditLinespast7years');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs BankcardUtilization
plt.subplot(2, 2, 4);
sb.scatterplot(data=loan, x='BankcardUtilization', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('BankcardUtilization');
plt.ylabel('BorrowerAPR');


plt.figure(figsize=[20, 25]);
plt.suptitle('BorrowerARP with Borrower personal information');

# BorrowerAPR vs BorrowerState
plt.subplot(4, 2, 1);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='BorrowerState', y='BorrowerAPR', color=base_color);
plt.xlabel('BorrowerState');
plt.ylabel('BorrowerAPR');
plt.xticks(rotation=90);

# BorrowerAPR vs EmploymentStatus
plt.subplot(4, 2, 2);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='EmploymentStatus', y='BorrowerAPR', color=base_color);
plt.xlabel('EmploymentStatus');
plt.ylabel('BorrowerAPR');
plt.xticks(rotation=10);

# BorrowerAPR vs IsBorrowerHomeowner
plt.subplot(4, 2, 3);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='IsBorrowerHomeowner', y='BorrowerAPR', color=base_color);
plt.xlabel('IsBorrowerHomeowner');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs OpenRevolvingMonthlyPayment
plt.subplot(4, 2, 4);
sb.scatterplot(data=loan, x='OpenRevolvingMonthlyPayment', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('OpenRevolvingMonthlyPayment');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs TradesOpenedLast6Months
plt.subplot(4, 2, 5);
sb.scatterplot(data=loan, x='TradesOpenedLast6Months', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('TradesOpenedLast6Months');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs MonthlyLoanPayment
plt.subplot(4, 2, 6);
sb.scatterplot(data=loan, x='MonthlyLoanPayment', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('MonthlyLoanPayment');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs DebtToIncomeRatio
plt.subplot(4, 2, 7);
sb.scatterplot(data=loan, x='DebtToIncomeRatio', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('DebtToIncomeRatio');
plt.ylabel('BorrowerAPR');
plt.xlim(0, 1);


plt.figure(figsize=[11, 11]);
plt.suptitle('BorrowerARP with Borrower credit information');

# BorrowerAPR vs TotalInquiries
plt.subplot(2, 2, 1);
sb.scatterplot(data=loan, x='TotalInquiries', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('TotalInquiries');
plt.ylabel('BorrowerAPR');
plt.xlim(0, 75);

# BorrowerAPR vs DelinquenciesLast7Years
plt.subplot(2, 2, 2);
sb.scatterplot(data=loan, x='DelinquenciesLast7Years', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('DelinquenciesLast7Years');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs PublicRecordsLast10Years
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='PublicRecordsLast10Years', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('PublicRecordsLast10Years');
plt.ylabel('BorrowerAPR');
plt.xlim(0, 30);


plt.figure(figsize=[11, 11]);
plt.suptitle('BorrowerARP with lender information');

# BorrowerAPR vs LP_ServiceFees
plt.subplot(2, 2, 1);
sb.scatterplot(data=loan, x='LP_ServiceFees', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('LP_ServiceFees');
plt.ylabel('BorrowerAPR');
plt.xlim(-500,0);

# BorrowerAPR vs Recommendations
plt.subplot(2, 2, 2);
sb.scatterplot(data=loan, x='Recommendations', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('Recommendations');
plt.ylabel('BorrowerAPR');
plt.xlim(-1,10)

# BorrowerAPR vs Investors
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='Investors', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('Investors');
plt.ylabel('BorrowerAPR');


plt.figure(figsize=[11, 11]);
plt.suptitle('BorrowerARP with payment information');

# BorrowerAPR vs OnTimeProsperPayments
plt.subplot(2, 2, 1);
sb.scatterplot(data=loan, x='OnTimeProsperPayments', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('OnTimeProsperPayments');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs ProsperPaymentsLessThanOneMonthLate
plt.subplot(2, 2, 2);
sb.scatterplot(data=loan, x='ProsperPaymentsLessThanOneMonthLate', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('ProsperPaymentsLessThanOneMonthLate');
plt.ylabel('BorrowerAPR');

# BorrowerAPR vs ProsperPaymentsOneMonthPlusLate
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='ProsperPaymentsOneMonthPlusLate', y='BorrowerAPR', alpha= 0.2);
plt.xlabel('ProsperPaymentsOneMonthPlusLate');
plt.ylabel('BorrowerAPR');


plt.figure(figsize=[11, 6]);
plt.suptitle('ProsperScore with Borrower credit information');

# ProsperScore vs TotalCreditLinespast7years
plt.subplot(1, 2, 1);
sb.scatterplot(data=loan, x='TotalCreditLinespast7years', y='ProsperScore', alpha= 0.2);
plt.xlabel('TotalCreditLinespast7years');
plt.ylabel('ProsperScore');

# ProsperScore vs BankcardUtilization
plt.subplot(1, 2, 2);
sb.scatterplot(data=loan, x='BankcardUtilization', y='ProsperScore', alpha= 0.2);
plt.xlabel('BankcardUtilization');
plt.ylabel('ProsperScore');
plt.xlim(0, 1);


plt.figure(figsize=[20, 25]);
plt.suptitle('ProsperScore with Borrower personal information');

# ProsperScore vs BorrowerState
plt.subplot(4, 2, 1);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='BorrowerState', y='ProsperScore', color=base_color);
plt.xlabel('BorrowerState');
plt.ylabel('ProsperScore');
plt.xticks(rotation=90);

# ProsperScore vs EmploymentStatus
plt.subplot(4, 2, 2);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='EmploymentStatus', y='ProsperScore', color=base_color);
plt.xlabel('EmploymentStatus');
plt.ylabel('ProsperScore');
plt.xticks(rotation=10);

# ProsperScore vs IsBorrowerHomeowner
plt.subplot(4, 2, 3);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='IsBorrowerHomeowner', y='ProsperScore', color=base_color);
plt.xlabel('IsBorrowerHomeowner');
plt.ylabel('ProsperScore')

# ProsperScore vs OpenRevolvingMonthlyPayment
plt.subplot(4, 2, 4);
sb.scatterplot(data=loan, x='OpenRevolvingMonthlyPayment', y='ProsperScore', alpha= 0.2);
plt.xlabel('OpenRevolvingMonthlyPayment');
plt.ylabel('ProsperScore');

# ProsperScore vs TradesOpenedLast6Months
plt.subplot(4, 2, 5);
sb.scatterplot(data=loan, x='TradesOpenedLast6Months', y='ProsperScore', alpha= 0.2);
plt.xlabel('TradesOpenedLast6Months');
plt.ylabel('ProsperScore');

# ProsperScore vs MonthlyLoanPayment
plt.subplot(4, 2, 6);
sb.scatterplot(data=loan, x='MonthlyLoanPayment', y='ProsperScore', alpha= 0.2);
plt.xlabel('MonthlyLoanPayment');
plt.ylabel('ProsperScore');

# ProsperScore vs DebtToIncomeRatio
plt.subplot(4, 2, 7);
sb.scatterplot(data=loan, x='DebtToIncomeRatio', y='ProsperScore', alpha= 0.2);
plt.xlabel('DebtToIncomeRatio');
plt.ylabel('ProsperScore');
plt.xlim(0, 1);


plt.figure(figsize=[11, 11]);
plt.suptitle('ProsperScore with payment information');

# ProsperScore vs OnTimeProsperPayments
plt.subplot(2, 2, 1);
sb.scatterplot(data=loan, x='OnTimeProsperPayments', y='ProsperScore', alpha= 0.2);
plt.xlabel('OnTimeProsperPayments');
plt.ylabel('ProsperScore');

# ProsperScore vs ProsperPaymentsLessThanOneMonthLate
plt.subplot(2, 2, 2);
sb.scatterplot(data=loan, x='ProsperPaymentsLessThanOneMonthLate', y='ProsperScore', alpha= 0.2);
plt.xlabel('ProsperPaymentsLessThanOneMonthLate');
plt.ylabel('ProsperScore');

# ProsperScore vs ProsperPaymentsOneMonthPlusLate
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='ProsperPaymentsOneMonthPlusLate', y='ProsperScore', alpha= 0.2);
plt.xlabel('ProsperPaymentsOneMonthPlusLate');
plt.ylabel('ProsperScore');


plt.figure(figsize=[11, 11]);
plt.suptitle('Investors with Borrower credit information');

# Investors vs CreditGrade
plt.subplot(2, 2, 1);
base_color =  sb.color_palette()[0]
sb.boxplot(data=df, x='CreditGrade', y='Investors', color=base_color);
plt.xlabel('CreditGrade');
plt.ylabel('Investors');

# Investors vs ProsperScore
plt.subplot(2, 2, 2);
sb.boxplot(data=loan, x='ProsperScore', y='Investors', color=base_color);
plt.xlabel('ProsperScore');
plt.ylabel('Investors');

# Investors vs TotalCreditLinespast7years
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='TotalCreditLinespast7years', y='Investors', alpha= 0.2);
plt.xlabel('TotalCreditLinespast7years');
plt.ylabel('Investors');

# Investors vs BankcardUtilization
plt.subplot(2, 2, 4);
sb.scatterplot(data=loan, x='BankcardUtilization', y='Investors', alpha= 0.2);
plt.xlabel('BankcardUtilization');
plt.ylabel('Investors');
plt.xlim(0, 1);


plt.figure(figsize=[20, 25]);
plt.suptitle('Investors with Borrower personal information');

# Investors vs BorrowerState
plt.subplot(4, 2, 1);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='BorrowerState', y='Investors', color=base_color);
plt.xlabel('BorrowerState');
plt.ylabel('Investors');
plt.xticks(rotation=90);

# Investors vs EmploymentStatus
plt.subplot(4, 2, 2);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='EmploymentStatus', y='Investors', color=base_color);
plt.xlabel('EmploymentStatus');
plt.ylabel('Investors');
plt.xticks(rotation=10);

# Investors vs IsBorrowerHomeowner
plt.subplot(4, 2, 3);
base_color =  sb.color_palette()[0]
sb.boxplot(data=loan, x='IsBorrowerHomeowner', y='Investors', color=base_color);
plt.xlabel('IsBorrowerHomeowner');
plt.ylabel('Investors')

# Investors vs OpenRevolvingMonthlyPayment
plt.subplot(4, 2, 4);
sb.scatterplot(data=loan, x='OpenRevolvingMonthlyPayment', y='Investors', alpha= 0.2);
plt.xlabel('OpenRevolvingMonthlyPayment');
plt.ylabel('Investors');

# Investors vs TradesOpenedLast6Months
plt.subplot(4, 2, 5);
sb.scatterplot(data=loan, x='TradesOpenedLast6Months', y='Investors', alpha= 0.2);
plt.xlabel('TradesOpenedLast6Months');
plt.ylabel('Investors');

# Investors vs MonthlyLoanPayment
plt.subplot(4, 2, 6);
sb.scatterplot(data=loan, x='MonthlyLoanPayment', y='Investors', alpha= 0.2);
plt.xlabel('MonthlyLoanPayment');
plt.ylabel('Investors');

# Investors vs DebtToIncomeRatio
plt.subplot(4, 2, 7);
sb.scatterplot(data=loan, x='DebtToIncomeRatio', y='Investors', alpha= 0.2);
plt.xlabel('DebtToIncomeRatio');
plt.ylabel('Investors');
plt.xlim(0, 1);


plt.figure(figsize=[11, 11]);
plt.suptitle('Investors with payment information');

# Investors vs OnTimeProsperPayments
plt.subplot(2, 2, 1);
sb.scatterplot(data=loan, x='OnTimeProsperPayments', y='Investors', alpha= 0.2);
plt.xlabel('OnTimeProsperPayments');
plt.ylabel('Investors');

# Investors vs ProsperPaymentsLessThanOneMonthLate
plt.subplot(2, 2, 2);
sb.scatterplot(data=loan, x='ProsperPaymentsLessThanOneMonthLate', y='Investors', alpha= 0.2);
plt.xlabel('ProsperPaymentsLessThanOneMonthLate');
plt.ylabel('Investors');

# Investors vs ProsperPaymentsOneMonthPlusLate
plt.subplot(2, 2, 3);
sb.scatterplot(data=loan, x='ProsperPaymentsOneMonthPlusLate', y='Investors', alpha= 0.2);
plt.xlabel('ProsperPaymentsOneMonthPlusLate');
plt.ylabel('Investors');


#payment correlation
plt.figure(figsize = [11, 8])
num_vars = ['LoanOriginalAmount', 'BorrowerAPR', 'MonthlyLoanPayment', 'DebtToIncomeRatio','OpenRevolvingMonthlyPayment']
sb.heatmap(loan[num_vars].corr(), annot = True, fmt = '.3f', cmap = 'Blues', center = 0);


# BorrowerAPR in terms of loan amount and duration
plt.figure(figsize=[11, 6]);
duration=sb.FacetGrid(data=loan, col='Term', height=5, col_wrap=3)
duration.map(sb.regplot, 'LoanOriginalAmount', 'BorrowerAPR', x_jitter=0.04, scatter_kws={'alpha':0.1});
duration.add_legend();

<Figure size 792x432 with 0 Axes>


# BorrowerAPR in terms of loan amount and duration
plt.figure(figsize=[11, 11]);
duration=sb.FacetGrid(data=df, col='CreditGrade', height=5, col_wrap=4)
duration.map(sb.regplot, 'LoanOriginalAmount', 'BorrowerAPR', x_jitter=0.04, scatter_kws={'alpha':0.1});
duration.add_legend();

<Figure size 792x792 with 0 Axes>


plt.figure(figsize=[15, 11]);
plt.suptitle('ProsperScore by loan duration');

plt.subplot(2, 2, 1);
sb.pointplot(data = loan, x = 'ProsperScore', y = 'BorrowerAPR', hue = 'Term', palette = 'Blues', linestyles = '', dodge = 0.4, ci='sd')
plt.xlabel('ProsperScore');
plt.ylabel('BorrowerAPR');

plt.subplot(2, 2, 2);
sb.pointplot(data = loan, x = 'ProsperScore', y = 'DebtToIncomeRatio', hue = 'Term', palette = 'Blues', linestyles = '', dodge = 0.4, ci='sd')
plt.xlabel('ProsperScore');
plt.ylabel('DebtToIncomeRatio');

plt.subplot(2, 2, 3);
sb.pointplot(data = loan, x = 'ProsperScore', y = 'LoanOriginalAmount', hue = 'Term', palette = 'Blues', linestyles = '', dodge = 0.4, ci='sd')
plt.xlabel('ProsperScore');
plt.ylabel('LoanOriginalAmount');

plt.subplot(2, 2, 4);
sb.pointplot(data = loan, x = 'ProsperScore', y = 'Investors', hue = 'Term', palette = 'Blues', linestyles = '', dodge = 0.4, ci='sd')
plt.xlabel('ProsperScore');
plt.ylabel('Investors');


plt.figure(figsize=[15, 6]);
plt.suptitle('LoanOriginationQuarter');

plt.subplot(1, 2, 1);
sb.countplot(data = loan, x = 'LoanOriginationQuarter', hue= 'Term', color = default_color)
plt.xticks(rotation=90);

plt.subplot(1, 2, 2);
sb.countplot(data = loan, x = 'LoanOriginationQuarter', hue= 'ProsperScore', color = default_color)
plt.xticks(rotation=90);


plt.suptitle('LoanOriginationQuarter by loan duration for ProsperScore');
g=sb.catplot(x="LoanOriginationQuarter", hue="Term", col="ProsperScore", data=loan, kind="count", height=4, col_wrap=3);
g.set_xticklabels(rotation=90);

<Figure size 432x288 with 0 Axes>


plt.figure(figsize=[15, 8]);
plt.suptitle('BorrowerAPR and EmploymentStatus for loan terms');

# BorrowerAPR and EmploymentStatus for loan 12 months duration 
plt.subplot(3, 1, 1)
duration = loan.query('Term == 12')
s = duration.groupby(['BorrowerAPR', 'EmploymentStatus']).size()
s = s.reset_index(name='count')
s = s.pivot(index='BorrowerAPR', columns='EmploymentStatus', values='count')
sb.heatmap(s, cmap='rocket_r');
plt.title('12', loc='left');
plt.xlabel('EmploymentStatus');
plt.ylabel('BorrowerAPR');

# BorrowerAPR and EmploymentStatus for loan 36 months duration 
plt.subplot(3, 1, 2)

duration = loan.query('Term == 36')
s = duration.groupby(['BorrowerAPR', 'EmploymentStatus']).size()
s = s.reset_index(name='count')
s = s.pivot(index='BorrowerAPR', columns='EmploymentStatus', values='count')
sb.heatmap(s, cmap='rocket_r');
plt.title('36', loc='left');
plt.xlabel('EmploymentStatus');
plt.ylabel('BorrowerAPR');
# BorrowerAPR and EmploymentStatus for loan 60 months duration 
plt.subplot(3, 1, 3)

duration = loan.query('Term == 60')
s = duration.groupby(['BorrowerAPR', 'EmploymentStatus']).size()
s = s.reset_index(name='count')
s = s.pivot(index='BorrowerAPR', columns='EmploymentStatus', values='count')
sb.heatmap(s, cmap='rocket_r');
plt.title('60', loc='left');
plt.xlabel('EmploymentStatus');
plt.ylabel('BorrowerAPR');


plt.figure(figsize = [12, 8])
sb.countplot(data = loan, x = 'ProsperScore', hue = 'EmploymentStatus', palette='Blues');


plt.figure(figsize=[15, 8]);
plt.suptitle('BorrowerAPR and ProsperRating for loan status');

# BorrowerAPR and ProsperRating for loan Current 
plt.subplot(3, 1, 1)
duration = loan.query('LoanStatus == "Current"')
s = duration.groupby(['BorrowerAPR', 'ProsperScore']).size()
s = s.reset_index(name='count')
s = s.pivot(index='BorrowerAPR', columns='ProsperScore', values='count')
sb.heatmap(s, cmap='rocket_r');
plt.title('Current', loc='left');
plt.xlabel('ProsperScore');
plt.ylabel('BorrowerAPR');

# BorrowerAPR and ProsperRating for loan Completed
plt.subplot(3, 1, 2)

duration = loan.query('LoanStatus == "Completed"')
s = duration.groupby(['BorrowerAPR', 'ProsperScore']).size()
s = s.reset_index(name='count')
s = s.pivot(index='BorrowerAPR', columns='ProsperScore', values='count')
sb.heatmap(s, cmap='rocket_r');
plt.title('Completed', loc='left');
plt.xlabel('ProsperScore');
plt.ylabel('BorrowerAPR');
# BorrowerAPR and ProsperRating for loan Chargedoff 
plt.subplot(3, 1, 3)

duration = loan.query('LoanStatus == "Chargedoff"')
s = duration.groupby(['BorrowerAPR', 'ProsperScore']).size()
s = s.reset_index(name='count')
s = s.pivot(index='BorrowerAPR', columns='ProsperScore', values='count')
sb.heatmap(s, cmap='rocket_r');
plt.title('Chargedoff', loc='left');
plt.xlabel('ProsperScore');
plt.ylabel('BorrowerAPR');

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	CurrentlyInGroup	GroupKey	DateCreditPulled	CreditScoreRangeLower	CreditScoreRangeUpper	FirstRecordedCreditLine	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	LoanKey	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	LoanOriginationDate	LoanOriginationQuarter	MemberKey	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	PercentFunded	Investors
0	1021339766868145413AB3B	193129	2007-08-26 19:09:29.263000000	C	36	Completed	2009-08-14 00:00:00	0.16516	0.1580	0.1380	NaN	NaN	NaN	NaN	NaN	NaN	0	CO	Other	Self-employed	2.0	True	True	NaN	2007-08-26 18:41:46.780000000	640.0	659.0	2001-10-11 00:00:00	5.0	4.0	12.0	1	24.0	3.0	3.0	2.0	472.0	4.0	0.0	0.0	0.0	0.00	1500.0	11.0	0.81	0.0	0.17	$25,000-49,999	True	3083.333333	E33A3400205839220442E84	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	78	19141	9425	2007-09-12 00:00:00	Q3 2007	1F3E3376408759268057EDA	330.43	11396.14	9425.00	1971.14	-133.18	1.0	258
1	10273602499503308B223C1	1209647	2014-02-27 08:28:07.900000000	NaN	36	Current	NaN	0.12016	0.0920	0.0820	0.07960	0.0249	0.05470	6.0	A	7.0	2	CO	Professional	Employed	44.0	False	False	NaN	2014-02-27 08:28:14	680.0	699.0	1996-03-18 00:00:00	14.0	14.0	29.0	13	389.0	3.0	5.0	0.0	0.0	0.0	1.0	0.0	3989.0	0.21	10266.0	29.0	1.00	2.0	0.18	$50,000-74,999	True	6125.000000	9E3B37071505919926B1D82	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0	134815	10000	2014-03-03 00:00:00	Q1 2014	1D13370546739025387B2F4	318.93	0.00	0.00	0.00	0.00	1.0	1
2	0EE9337825851032864889A	81716	2007-01-05 15:00:47.090000000	HR	36	Completed	2009-12-17 00:00:00	0.28269	0.2750	0.2400	NaN	NaN	NaN	NaN	NaN	NaN	0	GA	Other	Not available	NaN	False	True	783C3371218786870A73D20	2007-01-02 14:09:10.060000000	480.0	499.0	2002-07-27 00:00:00	NaN	NaN	3.0	0	0.0	0.0	1.0	1.0	NaN	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.06	Not displayed	True	2083.333333	6954337960046817851BCB2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	86	6466	3001	2007-01-17 00:00:00	Q1 2007	5F7033715035555618FA612	123.32	4186.63	3001.00	1185.63	-24.20	1.0	41
3	0EF5356002482715299901A	658116	2012-10-22 11:02:35.010000000	NaN	36	Current	NaN	0.12528	0.0974	0.0874	0.08490	0.0249	0.06000	6.0	A	9.0	16	GA	Skilled Labor	Employed	113.0	True	False	NaN	2012-10-22 11:02:32	800.0	819.0	1983-02-28 00:00:00	5.0	5.0	29.0	7	115.0	0.0	1.0	4.0	10056.0	14.0	0.0	0.0	1444.0	0.04	30754.0	26.0	0.76	0.0	0.15	$25,000-49,999	True	2875.000000	A0393664465886295619C51	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16	77296	10000	2012-11-01 00:00:00	Q4 2012	9ADE356069835475068C6D2	321.45	5143.20	4091.09	1052.11	-108.01	1.0	158
4	0F023589499656230C5E3E2	909464	2013-09-14 18:38:39.097000000	NaN	36	Current	NaN	0.24614	0.2085	0.1985	0.18316	0.0925	0.09066	3.0	D	4.0	2	MN	Executive	Employed	44.0	True	False	NaN	2013-09-14 18:38:44	680.0	699.0	2004-02-20 00:00:00	19.0	19.0	49.0	6	220.0	1.0	9.0	0.0	0.0	0.0	0.0	0.0	6193.0	0.81	695.0	39.0	0.95	2.0	0.26	$100,000+	True	9583.333333	A180369302188889200689E	1.0	11.0	11.0	0.0	0.0	11000.0	9947.9	NaN	NaN	6	102670	15000	2013-09-20 00:00:00	Q3 2013	36CE356043264555721F06C	563.97	2819.85	1563.22	1256.63	-60.27	1.0	20
5	0F05359734824199381F61D	1074836	2013-12-14 08:26:37.093000000	NaN	60	Current	NaN	0.15425	0.1314	0.1214	0.11567	0.0449	0.07077	5.0	B	10.0	1	NM	Professional	Employed	82.0	True	False	NaN	2013-12-14 08:26:40	740.0	759.0	1973-03-01 00:00:00	21.0	17.0	49.0	13	1410.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	62999.0	0.39	86509.0	47.0	1.00	0.0	0.36	$100,000+	True	8333.333333	C3D63702273952547E79520	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	123257	15000	2013-12-24 00:00:00	Q4 2013	874A3701157341738DE458F	342.37	679.34	351.89	327.45	-25.33	1.0	1
6	0F0A3576754255009D63151	750899	2013-04-12 09:52:56.147000000	NaN	36	Current	NaN	0.31032	0.2712	0.2612	0.23820	0.1275	0.11070	2.0	E	2.0	1	KS	Sales - Retail	Employed	172.0	False	False	NaN	2013-04-12 09:52:53	680.0	699.0	2000-09-29 00:00:00	10.0	7.0	20.0	6	214.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	5812.0	0.72	1929.0	16.0	0.68	0.0	0.27	$25,000-49,999	True	2083.333333	CE963680102927767790520	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	11	88353	3000	2013-04-18 00:00:00	Q2 2013	AA4535764146102879D5959	122.67	1226.70	604.25	622.45	-22.95	1.0	1
7	0F1035772717087366F9EA7	768193	2013-05-05 06:49:27.493000000	NaN	36	Current	NaN	0.23939	0.2019	0.1919	0.17830	0.0799	0.09840	4.0	C	4.0	2	CA	Laborer	Employed	103.0	False	False	NaN	2013-05-05 06:49:25	700.0	719.0	1999-02-25 00:00:00	6.0	6.0	10.0	5	101.0	3.0	16.0	0.0	0.0	0.0	1.0	0.0	1260.0	0.13	2181.0	10.0	0.80	0.0	0.24	$25,000-49,999	True	3355.750000	0C87368108902149313D53B	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	10	90051	10000	2013-05-13 00:00:00	Q2 2013	737F347089545035681C074	372.60	3353.40	1955.89	1397.51	-69.21	1.0	1
8	0F043596202561788EA13D5	1023355	2013-12-02 10:43:39.117000000	NaN	36	Current	NaN	0.07620	0.0629	0.0529	0.05221	0.0099	0.04231	7.0	AA	9.0	7	IL	Food Service	Employed	269.0	True	False	NaN	2013-12-02 10:43:39	820.0	839.0	1993-04-01 00:00:00	17.0	16.0	32.0	12	219.0	1.0	6.0	0.0	0.0	0.0	0.0	0.0	9906.0	0.11	77696.0	29.0	1.00	1.0	0.25	$25,000-49,999	True	3333.333333	02163700809231365A56A1C	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	121268	10000	2013-12-12 00:00:00	Q4 2013	49A53699682291323D04D66	305.54	611.08	505.58	105.50	-16.77	1.0	1
9	0F043596202561788EA13D5	1023355	2013-12-02 10:43:39.117000000	NaN	36	Current	NaN	0.07620	0.0629	0.0529	0.05221	0.0099	0.04231	7.0	AA	11.0	7	IL	Food Service	Employed	269.0	True	False	NaN	2013-12-02 10:43:39	820.0	839.0	1993-04-01 00:00:00	17.0	16.0	32.0	12	219.0	1.0	6.0	0.0	0.0	0.0	0.0	0.0	9906.0	0.11	77696.0	29.0	1.00	1.0	0.25	$25,000-49,999	True	3333.333333	02163700809231365A56A1C	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	121268	10000	2013-12-12 00:00:00	Q4 2013	49A53699682291323D04D66	305.54	611.08	505.58	105.50	-16.77	1.0	1

	ListingNumber	Term	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperScore	ListingCategory (numeric)	EmploymentStatusDuration	CreditScoreRangeLower	CreditScoreRangeUpper	CurrentCreditLines	OpenCreditLines	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	InquiriesLast6Months	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	PublicRecordsLast12Months	RevolvingCreditBalance	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	StatedMonthlyIncome	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	ProsperPrincipalBorrowed	ProsperPrincipalOutstanding	ScorexChangeAtTimeOfListing	LoanCurrentDaysDelinquent	LoanFirstDefaultedCycleNumber	LoanMonthsSinceOrigination	LoanNumber	LoanOriginalAmount	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	LP_CollectionFees	LP_GrossPrincipalLoss	LP_NetPrincipalLoss	LP_NonPrincipalRecoverypayments	PercentFunded	Recommendations	InvestmentFromFriendsCount	InvestmentFromFriendsAmount	Investors
count	1.139370e+05	113937.000000	113912.000000	113937.000000	113937.000000	84853.000000	84853.000000	84853.000000	84853.000000	84853.000000	113937.000000	106312.000000	113346.000000	113346.000000	106333.000000	106333.000000	113240.000000	113937.00000	113937.000000	113240.000000	112778.000000	113240.000000	106315.000000	112947.000000	113240.000000	106333.000000	1.063330e+05	106333.000000	106393.000000	106393.000000	106393.000000	106393.000000	105383.000000	1.139370e+05	22085.000000	22085.000000	22085.000000	22085.000000	22085.000000	22085.000000	22085.000000	18928.000000	113937.000000	16952.000000	113937.000000	113937.000000	113937.00000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000	113937.000000
mean	6.278857e+05	40.830248	0.218828	0.192764	0.182701	0.168661	0.080306	0.096068	4.072243	5.950067	2.774209	96.071582	685.567731	704.567731	10.317192	9.260164	26.754539	6.96979	398.292161	1.435085	5.584405	0.592052	984.507059	4.154984	0.312646	0.015094	1.759871e+04	0.561309	11210.225447	23.230034	0.885897	0.802327	0.275947	5.608026e+03	1.421100	22.934345	22.271949	0.613629	0.048540	8472.311961	2930.313906	-3.223214	152.816539	16.268464	31.896882	69444.474271	8337.01385	272.475783	4183.079489	3105.536588	1077.542901	-54.725641	-14.242698	700.446342	681.420499	25.142686	0.998584	0.048027	0.023460	16.550751	80.475228
std	3.280762e+05	10.436212	0.080364	0.074818	0.074516	0.068467	0.046764	0.030403	1.673227	2.376501	3.996797	94.480605	66.458275	66.458275	5.457866	5.022644	13.637871	4.63097	447.159711	2.437507	6.429946	1.978707	7158.270157	10.160216	0.727868	0.154092	3.293640e+04	0.317918	19818.361309	11.871311	0.148179	1.097637	0.551759	7.478497e+03	0.764042	19.249584	18.830425	2.446827	0.556285	7395.507650	3806.635075	50.063567	466.320254	9.005898	29.974184	38930.479610	6245.80058	192.697812	4790.907234	4069.527670	1183.414168	60.675425	109.232758	2388.513831	2357.167068	275.657937	0.017919	0.332353	0.232412	294.545422	103.239020
min	4.000000e+00	12.000000	0.006530	0.000000	-0.010000	-0.182700	0.004900	-0.182700	1.000000	1.000000	0.000000	0.000000	0.000000	19.000000	0.000000	0.000000	2.000000	0.00000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	-209.000000	0.000000	0.000000	0.000000	1.000000	1000.00000	0.000000	-2.349900	0.000000	-2.349900	-664.870000	-9274.750000	-94.200000	-954.550000	0.000000	0.700000	0.000000	0.000000	0.000000	1.000000
25%	4.009190e+05	36.000000	0.156290	0.134000	0.124200	0.115670	0.042400	0.074080	3.000000	4.000000	1.000000	26.000000	660.000000	679.000000	7.000000	6.000000	17.000000	4.00000	114.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.121000e+03	0.310000	880.000000	15.000000	0.820000	0.000000	0.140000	3.200333e+03	1.000000	9.000000	9.000000	0.000000	0.000000	3500.000000	0.000000	-35.000000	0.000000	9.000000	6.000000	37332.000000	4000.00000	131.620000	1005.760000	500.890000	274.870000	-73.180000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	2.000000
50%	6.005540e+05	36.000000	0.209760	0.184000	0.173000	0.161500	0.072400	0.091700	4.000000	6.000000	1.000000	67.000000	680.000000	699.000000	10.000000	9.000000	25.000000	6.00000	271.000000	1.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.000000	8.549000e+03	0.600000	4100.000000	22.000000	0.940000	0.000000	0.220000	4.666667e+03	1.000000	16.000000	15.000000	0.000000	0.000000	6000.000000	1626.550000	-3.000000	0.000000	14.000000	21.000000	68599.000000	6500.00000	217.740000	2583.830000	1587.500000	700.840100	-34.440000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	44.000000
75%	8.926340e+05	36.000000	0.283810	0.250000	0.240000	0.224300	0.112000	0.116600	5.000000	8.000000	3.000000	137.000000	720.000000	739.000000	13.000000	12.000000	35.000000	9.00000	525.000000	2.000000	7.000000	0.000000	0.000000	3.000000	0.000000	0.000000	1.952100e+04	0.840000	13180.000000	30.000000	1.000000	1.000000	0.320000	6.825000e+03	2.000000	33.000000	32.000000	0.000000	0.000000	11000.000000	4126.720000	25.000000	0.000000	22.000000	65.000000	101901.000000	12000.00000	371.580000	5548.400000	4000.000000	1458.540000	-13.920000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	115.000000
max	1.255725e+06	60.000000	0.512290	0.497500	0.492500	0.319900	0.366000	0.283700	7.000000	11.000000	20.000000	755.000000	880.000000	899.000000	59.000000	54.000000	136.000000	51.00000	14985.000000	105.000000	379.000000	83.000000	463881.000000	99.000000	38.000000	20.000000	1.435667e+06	5.950000	646285.000000	126.000000	1.000000	20.000000	10.010000	1.750003e+06	8.000000	141.000000	141.000000	42.000000	21.000000	72499.000000	23450.950000	286.000000	2704.000000	44.000000	100.000000	136486.000000	35000.00000	2251.510000	40702.390000	35000.000000	15617.030000	32.060000	0.000000	25000.000000	25000.000000	21117.900000	1.012500	39.000000	33.000000	25000.000000	1189.000000

	CreditGrade	Term	LoanStatus	BorrowerAPR	BorrowerRate	LenderYield	EstimatedEffectiveYield	EstimatedLoss	EstimatedReturn	ProsperRating (numeric)	ProsperRating (Alpha)	ProsperScore	ListingCategory (numeric)	BorrowerState	Occupation	EmploymentStatus	EmploymentStatusDuration	IsBorrowerHomeowner	TotalCreditLinespast7years	OpenRevolvingAccounts	OpenRevolvingMonthlyPayment	TotalInquiries	CurrentDelinquencies	AmountDelinquent	DelinquenciesLast7Years	PublicRecordsLast10Years	BankcardUtilization	AvailableBankcardCredit	TotalTrades	TradesNeverDelinquent (percentage)	TradesOpenedLast6Months	DebtToIncomeRatio	IncomeRange	IncomeVerifiable	StatedMonthlyIncome	TotalProsperLoans	TotalProsperPaymentsBilled	OnTimeProsperPayments	ProsperPaymentsLessThanOneMonthLate	ProsperPaymentsOneMonthPlusLate	LoanOriginalAmount	LoanOriginationQuarter	MonthlyLoanPayment	LP_CustomerPayments	LP_CustomerPrincipalPayments	LP_InterestandFees	LP_ServiceFees	PercentFunded	Investors
1	NaN	36	Current	0.12016	0.0920	0.0820	0.07960	0.0249	0.05470	6.0	A	7.0	2	CO	Professional	Employed	44.0	False	29.0	13	389.0	5.0	0.0	0.0	0.0	1.0	0.21	10266.0	29.0	1.00	2.0	0.18	$50,000-74,999	True	6125.000000	NaN	NaN	NaN	NaN	NaN	10000	Q1 2014	318.93	0.00	0.00	0.00	0.00	1.0	1
3	NaN	36	Current	0.12528	0.0974	0.0874	0.08490	0.0249	0.06000	6.0	A	9.0	16	GA	Skilled Labor	Employed	113.0	True	29.0	7	115.0	1.0	4.0	10056.0	14.0	0.0	0.04	30754.0	26.0	0.76	0.0	0.15	$25,000-49,999	True	2875.000000	NaN	NaN	NaN	NaN	NaN	10000	Q4 2012	321.45	5143.20	4091.09	1052.11	-108.01	1.0	158
4	NaN	36	Current	0.24614	0.2085	0.1985	0.18316	0.0925	0.09066	3.0	D	4.0	2	MN	Executive	Employed	44.0	True	49.0	6	220.0	9.0	0.0	0.0	0.0	0.0	0.81	695.0	39.0	0.95	2.0	0.26	$100,000+	True	9583.333333	1.0	11.0	11.0	0.0	0.0	15000	Q3 2013	563.97	2819.85	1563.22	1256.63	-60.27	1.0	20
5	NaN	60	Current	0.15425	0.1314	0.1214	0.11567	0.0449	0.07077	5.0	B	10.0	1	NM	Professional	Employed	82.0	True	49.0	13	1410.0	2.0	0.0	0.0	0.0	0.0	0.39	86509.0	47.0	1.00	0.0	0.36	$100,000+	True	8333.333333	NaN	NaN	NaN	NaN	NaN	15000	Q4 2013	342.37	679.34	351.89	327.45	-25.33	1.0	1
6	NaN	36	Current	0.31032	0.2712	0.2612	0.23820	0.1275	0.11070	2.0	E	2.0	1	KS	Sales - Retail	Employed	172.0	False	20.0	6	214.0	0.0	0.0	0.0	0.0	0.0	0.72	1929.0	16.0	0.68	0.0	0.27	$25,000-49,999	True	2083.333333	NaN	NaN	NaN	NaN	NaN	3000	Q2 2013	122.67	1226.70	604.25	622.45	-22.95	1.0	1

(Prosper Loan Data Set)¶

by (Ahmed Khaled Mohamed)¶

Preliminary Wrangling¶

1.1. Explore the dataset¶

1.2. Check for the misleading rows¶

1.3. Clean the data¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

2.1. BorrowerAPR Distribution¶

2.2. Loan Original Amount Distribution¶

2.3. On Time Prosper Payments Distribution¶

2.4. Monthly Loan Payment Ditribution¶

2.5. Debt To Income Ratio Ditribution¶

2.6. Loan Origination Quarter Ditribution¶

2.7. Term Ditribution¶

2.8. Credit Grade Ditribution¶

2.9. Loan Status Ditribution¶

2.10. Prosper Score Ditribution¶

2.11. Borrower State Ditribution¶

2.12. Income Verifiable Ditribution¶

2.13. Is Borrower Homeowner Ditribution¶

2.14. Occupation Ditribution¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶

Bivariate Exploration¶

3.1. BorrowerAPR¶

3.1.1. BorrowerARP with Borrower credit information¶

3.1.2. BorrowerARP with Borrower personal information¶

3.1.3. BorrowerARP with Borrower records information¶

3.1.4. BorrowerARP with lender information¶

3.1.5. BorrowerARP with payment information¶

3.2. ProsperScore¶

3.2.1. ProsperScore with Borrower credit information¶

3.2.2. ProsperScore with Borrower personal information¶

3.2.3. ProsperScore with payment information¶

3.3. Investors¶

3.3.1. Investors with Borrower credit information¶

3.3.2. Investors with Borrower personal information¶

3.3.3. Investors with payment information¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

4.1. payment correlation¶

4.2. BorrowerAPR in terms of loan amount and duration¶

4.3. BorrowerAPR in terms of loan amount and duration¶

4.4. ProsperScore by loan duration¶

4.5. Loan Origination Quarter¶

4.6. LoanOriginationQuarter for each ProsperScore¶

4.7. BorrowerAPR and EmploymentStatus for loan terms¶

4.8. ProsperScore and EmploymentStatus¶

4.9. BorrowerAPR and ProsperRating for loan status¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Were there any interesting or surprising interactions between features?¶